Neuron Output Realigning Synchronization Module for ANN Efficiency
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Solution Overview
Problem
Existing solutions for accelerating artificial neural network (ANN) computations, such as GPUs and FPGAs, face challenges in power consumption, programming complexity, and inefficient data processing, particularly in handling large-scale parallel operations and massive data movement required for industrial applications.
Innovation Solution
A system and method for realigning streams of neuron outputs using a synchronization module to order and align neuron outputs from multiple processing units, reducing unnecessary mathematical operations by selectively processing input values and dynamically aligning results, thereby accelerating ANN computations on integrated circuits or FPGAs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If GPUs are used to process large ANNs to overcome throughput limitations, then computational throughput is improved, but power consumption increases excessively
Solution Approach 1:
The patent segments the neural network computation into multiple processing units (e.g., multiple FPGAs or CPU cores) that work in parallel. Each processing unit handles a subset of neurons or operations, distributing the computational load across multiple lower-power devices rather than concentrating it in a single high-power GPU, thereby reducing overall power consumption while maintaining throughput
Solution Approach 2:
The patent implements dynamic task allocation and data distribution strategies where processing units adaptively receive and process data packets based on current computational needs. This dynamic approach allows the system to optimize power usage by activating only the necessary processing units for each computation batch, rather than continuously operating all units at full power
2Productivity
If FPGAs are configured to perform ANN computations in parallel, then computational efficiency is improved, but programming complexity increases significantly
Solution Approach 1:
The patent develops a universal programming framework and abstraction layer that enables FPGAs to perform various ANN computations (different network architectures, layers, and operations) using a single standardized interface. This universal approach allows the same high-level code to be deployed across different FPGA configurations and ANN types, significantly reducing programming complexity while maintaining parallel computational efficiency
Solution Approach 2:
The patent introduces an intermediary software layer (such as a compiler or runtime system) that translates high-level neural network descriptions into optimized FPGA-specific configurations. This intermediary handles the complexity of parallelization, memory management, and resource allocation automatically, shielding the user from low-level FPGA programming complexities while enabling efficient parallel computation
3Speed
If multiple processing units generate neuron outputs in parallel, then computation speed is improved, but data alignment becomes difficult
Solution Approach 1:
The patent implements preliminary synchronization mechanisms where processing units exchange timing information and data metadata before actual computation begins. This preliminary action allows the system to pre-calculate data dependency relationships and establish synchronization barriers, ensuring that parallel processing units can operate independently at high speed while automatically aligning their outputs without complex post-processing
Solution Approach 2:
The patent employs feedback-based synchronization where processing units continuously monitor each other's computation progress and dynamically adjust their processing pace. Through feedback signals and acknowledgment protocols, faster units can wait for slower units or re-synchronize at designated checkpoints, maintaining data alignment across parallel processors while minimizing idle time and preserving overall computation speed
Data Source
AI summary
Systems and methods for realigning streams of neuron outputs are provided. An example method may include generating, by a processing unit, neuron outputs including at least a first neuron output and a second neuron output, generating, by at least one further processing unit, further neuron outputs including at least a further first neuron output and a further second neuron output, receiving, by a synchronization module communicatively coupled to the processing unit and the further processing unit, the neuron outputs, wherein the neuron outputs and the further neuron outputs are received in an arbitrary order, and ordering, by the synchronization module, the first neuron output, the further first neuron output, the second neuron output and the further second neuron output according to a further order, the further order being different from the arbitrary order.